Method and device for analyzing a technical system, in addition to a computer program product

ABSTRACT

The inventive system is used to design a technical system, which is characterized by condition variables and by diagnostic variables. A measurement field comprising first measured variables is incorporated into the design of the technical system, said first measured variables being measured with a predetermined accuracy. In addition, second measured variables can be measured with a predetermined accuracy. According to the inventive method, sensitivity variables are determined for the first measured variables. To determine said sensitivity variables, the extent to which a modification of the measurement accuracy of the first measured variables influences at least one parameter is calculated and to determine the second sensitivity variables, the extent to which the measurement of the second measured variables influences at least one parameter is calculated. The measurement field is then modified in such a way that the accuracy of the measured variables is altered, the first measured variables are removed from the measurement field and/or the second measured variables are added to the measurement field.

CROSS REFERENCE TO RELATED APPLICATION

This application is the US National Stage of International ApplicationNo. PCT/IP2003/012754, filed Nov. 14, 2003 and claims the benefitthereof. The International Application claims the benefits of EuropeanPatent application No. 10255959.7 DE filed Nov. 29, 2002, both of theapplications are incorporated by reference herein in their entirety.

FIELD OF THE INVENTION

The invention relates to a method and a device for designing a technicalsystem, and a corresponding computer program product.

BACKGROUND OF THE INVENTION

In the description of a technical system, for example a power station,reference is made to various parameters such as, for example, pressures,mass flows, etc. The parameters obey certain physical laws, such as forexample those of mass or energy balances, which can be expressed by asystem of equations. The solutions of this system of equations are thestate variables of the technical system. From these state variables itis possible in turn to calculate diagnostic variables relevant for theoperation of the technical system, such as for example the efficiency ofa power station. The specific state of a technical system can,furthermore, be sensed by measurements. The measured variables of themeasurements could directly reflect the value of a state variable; butit is also possible for measured variables which are derived from thestate variables to be measured. For example, it is possible to measurethe temperature of a technical system whereas the actual system statevariables are the enthalpy and the pressure. In order to determine thestate variables from the measured variables, one generally carries out ameasurement and looks for the values of the state variables which solvethe system of equations, and for which the derived measured variableslie closest to the measured values determined by the measurement (seefor example VDI Guideline 2048).

The problem can arise, due to the number of equations in the system ofequations being too small, or too small a number of measurement points,that individual state variables or individual diagnostic variablesremain indeterminate. In addition, the state variables or diagnosticvariables, as applicable, may be subject to great uncertainty, becauseof measurement errors. It is therefore necessary to decide whichmeasurements would permit the precision of certain state variables to beimproved, or would permit any determination at all of certain statevariables. For this purpose, it is usual to fall back on the advice ofexperienced engineers, and the suggestions of these engineers can bechecked by simulation programs. However, this requires time-consuminganalyses.

SUMMARY OF THE INVENTION

The object of the invention is therefore to specify a method fordesigning a technical system by which a systematic determination is madeof how the measurements of individual measured variables influence theparameters of the technical system.

This object is achieved in accordance with the features of theindependent claims. Developments of the invention are defined in thesubclaims.

The method in accordance with the invention is for use in designing atechnical system which is characterized by parameters, comprising statevariables and diagnostic variables which are dependent on the statevariables. Here, the term designing means, in particular, the analysisand/or changing of the technical system, in particular the analysis andchanging of the measurements made in the technical system. In thissituation, the technical system is specified by a system of equations,whereby the state variables are the solution of the system of equations.Incorporated into the design of the technical system is a measurement‘park’ which includes first measured variables, whereby these firstmeasured variables are measured in the technical system with aprescribed precision. In addition, second measured variables, which aredependent on the state variables, can be measured in the technicalsystem with a prescribed accuracy.

In the method in accordance with the invention, first sensitivityvariables are determined for the first measured variables and/or secondsensitivity variables are determined for the second measured variableswhereby, for the purpose of defining the first sensitivity variables, adetermination is made of the extent to which a change in the accuracy ofthe measurement of the first measured variables influences at least oneselected parameter, and for the purpose of defining the secondsensitivity variables, a determination is made of the extent to whichthe measurement of the second measured variables influences at least oneselected parameter. The measurement park is then amended, depending onthe first and/or the second sensitivity variables, in such a way thatthe accuracy of one or more of the first measured variables is changed,and/or one or more of the first measured variables is taken out of themeasurement park and/or one or more of the second measured variables isadded into the measurement park. This amended measurement park is usedfor designing the technical system.

In a preferred form of embodiment, the accuracy of a first measuredvariable is preferably increased, if the first sensitivity variable forthis measured variable lies within a predefined value range, and/or afirst measured variable is taken out of the measurement park if thefirst sensitivity variable for this measured variable lies within apredefined value range and/or a second measured variable is added intothe measurement park if the second sensitivity variable for thismeasured variable lies within a predefined value range. It is thuspossible in a simple manner, by the choice of different value ranges, tomodify the design procedure appropriately for various user-specificrequirements.

In a preferred form of embodiment, the technical system is specified bya system of equations H(x)=(H1(x) . . . , Hn(x)=0, where x=(x1, . . .xn) is a vector which includes as components the state variables xi. Itis noted at this point that all the indices i, j, k or l used belowrepresent cardinal numbers.

For the purpose of carrying out the method in accordance with theinvention in a preferred form of embodiment the following matrices are,in particular, calculated:

-   -   a matrix N which spans the null space of the Jacobian matrix H,    -   a matrix W, such that W^(T)·W is the inverse of the covariance        matrix of the first measured variables y_(i)=b_(i)(x), where the        covariance matrix has as its entries the covariances σ_(ij)        ²=E((y_(i)−E(y_(i)))(y_(j)−E(y_(j)))), where E(y) is the        expected value of y;    -   a matrix M which is the pseudoinverse matrix of A=W·Db·N, where        Db is the Jacobian matrix of the first measured variables        y_(i)=b_(i)(x).

The terms null space, Jacobian matrix and inverse or pseudoinversematrix, as applicable, have definitions which are familiar from thetheory of matrix computations (see for example Gene H. Golub, Charles F.van Load: “Matrix Computations”, 3^(rd) Edition, Baltimore, London; TheJohns Hopkins University Press; 1996).

In a further preferred form of embodiment of the invention, the firstsensitivity variables calculated in the technical system are in eachcase the ratio of the change in accuracy of a selected parameter to thechange in accuracy of a first measured variable, where the selectedparameter is a selected state variable, which can be determined via thefirst measured variables. The method is distinguished in this case bythe fact that:

-   -   at least one of the selected parameters is a selected state        variable, which can be determined via the first measured        variables;    -   one or more of the first sensitivity variables Φ_(yjx1)        represents in each case the ratio of the change in accuracy Δσ₁₁        ²/x₁=ΔE((x₁−E(x₁))²)/x₁ of the selected state variable x₁ to the        change in accuracy Δσ_(jj) ²/y_(j)=ΔE((y_(j)−E(y_(j)))²)/y_(j)        of a first measured variable y_(j);    -   the first sensitivity variables are determined from the        following formula:

$\Phi_{y_{j}x_{l}} = {\frac{\sigma_{jj}^{2}}{\sigma_{ll}^{2}} \cdot r_{lj}^{2}}$

where r_(lj) is the element in the l^(th) line and the j^(th) column ofthe matrix N·M·W.

In a further form of embodiment, each of the first sensitivity variablesrepresents the ratio of the change in the accuracy of a selecteddiagnostic variable to the change in the accuracy of a first measuredvariable, where the selected diagnostic variable can be determined viathe first measured variables. In this case, the method is distinguishedby the fact that:

-   -   at least one of the selected parameters is a selected diagnostic        variable, which can be determined via the first measured        variables;    -   a matrix Dd is determined, this being the Jacobian matrix of the        diagnostic variables d_(i)=d_(i)(x);    -   one or more of the first sensitivity variables Φ_(yj dn)        represents in each case the ratio of the change in accuracy        Δσ_(nn) ²/d_(n)=ΔE((d_(n)−E(d_(n)))²)/d_(n) of the selected        diagnostic variable d_(n) to the change in accuracy Δσ_(ij)        ²/y_(j)=ΔE((y_(j)−E(y_(j)))²)/y_(j) of a first measured variable        y_(j);    -   the first sensitivity variables are determined by the following        formula:

$\Phi_{y_{j}d_{n}} = {\frac{\sigma_{jj}^{2}}{\sigma_{nn}^{2}} \cdot s_{nj}^{2}}$

where s_(nj) is the element in the n^(th) line and the j^(th) column ofDd·N·M·W.

In a further preferred form of embodiment, one or more of the secondsensitivity variables each represents the variance of a selected statevariable when a second measured variable is being added in, where theselected state variable can be determined via the first measuredvariables. The method is distinguished in this case by the fact that:

-   -   at least one of the selected parameters is a selected state        variable which can be determined via the first measured        variables;    -   one or more of the second sensitivity variables represents, in        each case, the variance σ_(k→xl) ² of the selected state        variable x_(l) when a second measured variable, the value of        which is a state variable x_(k) with the variance σ_(k), is        being added to the measurement park;    -   the second sensitivity variables are determined by the following        formula:

${\sigma_{{k->{xl}} =}^{2}{m_{l}^{T} \cdot m_{l}}} - \frac{\left( {m_{k}^{T} \cdot m_{l}} \right)^{2}}{\sigma_{k}^{2} + {m_{k}^{T} \cdot m_{k}}}$

where m_(i) is the i^(th) column of the matrix M^(T)·N.

In a further form of embodiment of the invention, one or more of thesecond sensitivity variables represents in each case the variance of aselected diagnostic variable when a second measured variable is beingadded in, where the selected diagnostic variable can be determined viathe first measured variables. Here, the method is distinguished by thefact that:

-   -   at least one of the selected parameters is a selected diagnostic        variable which can be determined via the first measured        variables;    -   a matrix Dd, which is the Jacobian matrix of the diagnostic        variables d_(i)=d_(i)(x), is determined;    -   one or more of the second sensitivity variables represents, in        each case, the variance σ_(k→dn) ² of the selected diagnostic        variable d_(n) when a second measured variable, the value of        which is a state variable x_(k) and which has a variance σ_(k)        is being added to the measurement park;    -   the second sensitivity variables are determined by the following        formula:

$\sigma_{k->{dn}}^{2} = {{q_{n}^{T} \cdot q_{n}} - \frac{\left( {m_{k}^{T} \cdot q_{n}} \right)^{2}}{\sigma_{k}^{2} + {m_{k}^{T} \cdot m_{k}}}}$

where m_(i) is the i^(th) column of the matrix M^(T)·N^(T), and q_(n) isthe n^(th) column of the matrix and M^(T)·N^(T)·Dd^(T).

The case can now arise in which the selected parameter of the technicalsystem is a state variable which cannot be determined via the firstmeasured variables. In this case, the first step is to determine asecond measured variable, the value of which is a state variable, andwhich is to be added into the measurement park to enable the selectedparameter to be uniquely determined. The method by which this case istaken into account is distinguished by the fact that:

-   -   at least one of the selected parameters is a selected state        variable which cannot be determined via the first measured        variables;    -   a matrix P, which is the orthogonal projection onto the null        space of A, is determined;    -   a second measured variable is determined, the value of which is        a state variable x_(k), and which is to be added into the        measurement park so that the selected state variable can be        uniquely determined;    -   one of the second sensitivity variables represents the variance        σ_(k→xl) ² of the selected state variable when the second        measured variable x_(k) which has been determined, and which has        the variance σ_(k), is being added to the measurement park;    -   the second sensitivity variable is determined by the following        formula:

${\sigma_{k - {xl}}^{2} = {{\sigma_{k}^{2} \cdot \frac{{p}^{2}}{{p_{k}}^{2}}} + {{m_{l} - {\frac{p}{p_{k}}m_{k}}}}^{2}}},$

with p=Pn_(l), where n_(l) is the l^(th) column of the matrix N^(T), andm_(i) is the i^(th) column of the matrix M^(T)·N^(T) and p_(k) is thek^(th) column of the matrix P·N^(T).

The further case can arise in which the selected parameter is adiagnostic variable which cannot be determined via the first measuredvariable. In this case, the first step is to determine a second measuredvariable, the value of which is a state variable, and which is to beadded into the measurement park. The method by which this case is takeninto account is distinguished by the fact that:

-   -   at least one of the selected parameters is a selected diagnostic        variable which cannot be determined via the first measured        variables;    -   a matrix Dd, which is the Jacobian matrix of the diagnostic        variables d_(i)=d_(i)(x), is determined;    -   a matrix P, which is the orthogonal projection onto the null        space of A, is determined;    -   a second measured variable is determined, the value of which is        a state variable x_(k), and which is to be added into the        measurement park so that the selected diagnostic variable can be        uniquely determined;    -   one of the second sensitivity variables represents the variance        σ_(k→dn) ² of the selected diagnostic variable d_(n) when the        second measured variable x_(k) which has been determined, and        which has the variance σ_(k), is being added into the        measurement park;    -   the second sensitivity variable is determined by the following        formula:

${\sigma_{k - {dn}}^{2} = {{\sigma_{k}^{2} \cdot \frac{{p}^{2}}{{p_{k}}^{2}}} + {{{M^{T} \cdot c_{n}} - {\frac{p}{p_{k}}m_{k}}}}^{2}}},$

with p=Pc_(n), where c_(n) is the n^(th) column of the matrixN^(T)·Dd^(T), m_(k) is the k^(th) column of the matrix M^(T)·N^(T) andp_(k) is the k^(th) column of the matrix P·N^(T).

In the last two mentioned forms of embodiment, the determination of thesecond measured variable is preferably made by searching the matrixP·N^(T) for the column for which p is linearly dependent on the vectorwhich the column represents, and the index of this column then gives thesecond measured variable which is to be added to the measurement park.

In a particularly preferred embodiment of the invention, in those formsof embodiment for which the variance of selected parameters isdetermined as the second sensitivity variable when second measuredvariables are being added in, 1% of the value of the second measuredvariable is taken as the standard deviation for the second measuredvariables which are to be added in.

It should be remarked at this point that the correctness of all theformulae used above can be demonstrated mathematically.

In addition to the method described above, the invention also relates toa device for carrying put the method in accordance with the invention.Furthermore, the invention covers a computer program product which has astorage medium on which is stored a computer program, which can beexecuted on a computer and by which the method in accordance with theinvention can be performed.

BRIEF DESCRIPTION OF THE DRAWINGS

Exemplary embodiments of the invention are explained and illustratedbelow by reference to the drawings

These show:

FIG. 1 the schematic structure of a technical system which is analyzedby means of the method in accordance with the invention.

FIG. 2 a processor unit for carrying out the method in accordance withthe invention.

DETAILED DESCRIPTION OF THE INVENTION

The technical system shown in FIG. 1 relates to a heating system in apower station, with two heating surfaces 1 and 2 connected one after theother, with a gas stream G and a water stream W flowing past theseheating surfaces in opposite directions from each other.

The technical system is characterized by the following state variables:

m_(W,in) mass flow of the water when it enters into the heating system;

m_(W,out) mass flow of the water when it leaves the heating system;

h_(W,in) specific enthalpy of the water when it enters into the heatingsystem;

h_(W,middle) specific enthalpy of the water between the two heatingsurfaces 1 and 2;

h_(W,out) specific enthalpy of the water when it leaves the heatingsystem;

m_(G) mass flow of the gas in the heating system;

h_(G,in) specific enthalpy of the gas when it enters into the heatingsystem;

h_(G,middle) specific enthalpy of the gas between the two heatingsurfaces 1 and 2;

h_(G,out) specific enthalpy of the gas when it leaves the heatingsystem.

The state variables are the variables in a system of equations H(x)=0,which includes the following physical balance equations:

Mass balance for the water in the heating system:m _(W,in) −m _(W,out)=0;

Enthalpy balance at the first heating surface:m _(G)·(h _(G,in) −h _(G,middle))−m _(W,out)·(h _(W,out) −h_(W,middle))=0;

Enthalpy balance at the second heating surface:m _(G)·(h _(G,middle) −h _(G,out))−m _(W,in)·(h _(W,middle) −h_(W,in))=0.

The following set operating points of the technical system areconsidered, where the values shown below for the state variablesrepresent a solution to the above system of equations:

m_(W,in) m_(W,out) h_(W,in) h_(W,middle) h_(W,out) m_(G) h_(G,in)h_(G,middle) h_(G,out) 100 100 200 300 400 50 1000 800 600

Apart from the state variables identified above, the technical system isfurther characterized by a diagnostic value which, in the present case,represents the relative heat transfer of the gas which is flowingthrough. This heat transfer W can be described by the following formula:

$W = \frac{h_{G,{in}} - h_{G,{out}}}{h_{G,{in}}}$

The following first measured variables are measured in the technicalsystem with a standard deviation in each case of 1% relative to thesetpoint value concerned:

Enthalpy flow for the water on entry into the heating system:m_(W,in)·h_(W,in);

Mass flow of the water on entry into the heating system:m_(W,in)

Enthalpy flow for the water on leaving the heating system:m_(W,out)·h_(W,out);mass flow of the gas:m_(G);

Enthalpy flow of the gas on entry into the heating system:m_(G)·h_(G,in)

Using the formula quoted above for the relative heat transfer W with thesetpoint values gives W=0.4.

Because the standard deviations used are 1%, the measurement of therelative heat transfer leads to a measured value of 0.4 with a standarddeviation of 0.0098.

In a first variant of the method in accordance with the invention, thesensitivity variable used for a first measured quantity is in each casethe ratio of the change in accuracy of the diagnostic value W to thechange in accuracy of the first measured value.

This gives the following values:

Sensitivity variable for the enthalpy flow of the water on entry intothe heating system: 0.167.

Sensitivity variable for the mass flow of the water on entry into theheating system: 0.0.

Sensitivity variable for the enthalpy flow of the water on leaving theheating system: 0.667.

Sensitivity variable for the mass flow of the gas: 0.0

Sensitivity variable for the enthalpy flow of the gas on entry into theheating system: 0.167.

It will be seen that the sensitivity variable for the enthalpy flow ofthe water on leaving the heating system has the greatest value. Thismeans that a change in the accuracy of the measurement of the enthalpyflow of the water on leaving the heating system has the greatestinfluence on the accuracy of the measurement of the relative heattransfer. It follows that an improvement in the measurement accuracy ofthe enthalpy flow of the water on leaving the heating system will bemost effective in producing an improvement in the accuracy of thediagnostic value. By contrast, the measurements of the mass flows have asensitivity value of 0, and thus have no affects on the accuracy of thediagnostic value W.

In a further form of embodiment of the method in accordance with theinvention, the variances of the relative heat transfer are calculated asthe sensitivity variables, on the assumption that in the technicalsystem a state variable is being added in to the first measuredvariables as a second measured variable with a standard deviation of 1%relative to the setpoint value. Below are given the standard deviations(square roots of the variances) when individual state variables arebeing added in:

Add in m_(W,in):0.0098;

Add in m_(W,out):0.0098;

Add in h_(W,in):0.0095;

Add in h_(W,out):0.0086;

Add in m_(G):0.0098;

Add in h_(G,in):0.0095;

Add in h_(G,out):0.0062;

The addition of h_(W,middle) and h_(G,middle) is not considered becausethese state variables are not uniquely determined by the measuredvariables. It can be seen that the introduction of the measurement ofh_(G,out) gives the smallest standard deviation for the relative heattransfer. As a consequence, the measurement of h_(G,out) is added intothe measurement park for the first measurements.

In a further form of embodiment of the invention, consideration is nowgiven to state variables which are not uniquely determined by the firstmeasured variables of the technical system. In this case, these are thestate variables h_(W,middle) and h_(G,middle). A first step is now usedto determine the measured variables which must be added in for the statevariables h_(W,middle) and h_(G,middle) to be uniquely determined. Forthis purpose, a calculation is performed in accordance with claim 11.

It turns out that a measurement of h_(W,middle) or h_(G,middle) is ineach case sufficient to determine the two state variables h_(W,middle)and h_(G,middle). Making the assumption of a standard deviation of 1%for the measurement of h_(W,middle) or h_(G,middle), as applicable,gives:

-   -   in the case of a measurement of h_(W,middle), a standard        deviation for h_(W,middle) of 3.0, and a standard deviation for        h_(G,middle) of 17.34;    -   in the case of a measurement of h_(G,middle), a standard        deviation for h_(W,middle) of 9.06, and a standard deviation for        h_(G,middle) of 8.0.

From this it can be seen that for an exact determination of h_(W,middle)it is preferable to actually measure h_(W,middle), whereas for an exactdetermination of h_(G,middle) it is preferable to add also h_(G,middle)as a measurement in the measurement park.

The method described above permits a systematic and rapid search formeasurement points by which the accuracy of selected state variables ordiagnostic variables, as applicable, can be improved. It is thus nolonger necessary to fall back on the experience of engineers in order todecide which measured variables should be added into a measurement park,or which measurement accuracies should preferably be improved, asapplicable.

FIG. 2 shows a processor unit PRZE for performing the method inaccordance with the invention. The processor unit PRZE incorporates aprocessor CPU, a memory MEM, and an input/output interface IOS, which isused in various ways via an interface IFC: an output is shown visuallyon a monitor MON via a graphic interface, and/or is output on a printer.Inputs are made via a mouse MAS or a keyboard TAST. The processor unitPRZE also provides a data bus BUS, which establishes the link between amemory MEM, the processor CPU and the input/output interface IOS.Further, additional components can be connected to the data bus, forexample additional memory, data storage (hard disk) or scanners.

1. A method for designing a technical system, including state variablesand diagnostic variables that depend on the state variables, comprising:specifying the technical system by a system of equations and with thestate variables being the solutions of the system of equations;analyzing a measurement park, incorporating a first measured variableand the first measured variable is measured in the technical system witha prescribed accuracy and depend on the state variables; measuring asecond measured variable, which depends on the state variables, in thetechnical system with a predetermined accuracy; determining sensitivityvariables for the first measured variable and/or the second sensitivityvariable for the second measured variable; determining the magnitude ofthe influence which a change in the accuracy of measurement of the firstmeasured variable has at least one selected parameter to determine thefirst sensitivity variable, and to determine the second sensitivityvariable, a determination is made of the magnitude of the influencewhich the measurement of the second measured variable has at least oneselected parameter; changing the measurement park to produce an amendedmeasurement park, the changing depending on the first and/or secondsensitivity variable, in such a way that the accuracy of the firstmeasured variable is changed and/or the first measured variable is takenout of the measurement park and/or the second measured variable is addedinto the measurement park; and outputting a signal indicative of theamended measurement park for performing at least one of the following todesign the technical system: selecting which respective state variablesand/or diagnostic variables of the technical system to measure, anddetermining respective measurement accuracies for state variables and/ordiagnostic variables of the technical system.
 2. The method inaccordance with claim 1, wherein the accuracy of the first measuredvariable is increased if the first sensitivity variable for thismeasured variable lies within a predefined value range and/or the firstmeasured variable is taken out of the measurement park if the firstsensitivity variable for this measured variable lies within a predefinedvalue range and/or the second measured variable is added into themeasurement park if the second sensitivity variable for this measuredvariable lies within a predefined value range.
 3. The method inaccordance with claim 2, wherein the technical system is described by asystem of equations H(x)=(H₁(x), . . . , H_(m)(x))=0, where x=(x₁, . . ., x_(n)) is a vector in which the components are the state variablesx_(i).
 4. The method in accordance with claim 3, wherein the followingmatrices are calculated: a matrix N, which spans the null space of theJacobian matrix of H, a matrix W, such that W^(T)·W is the inverse ofthe covariance matrix of the first measured variables y_(i)=b_(i)(x),where the entries in the covariance matrix are the covariances σ_(ij)²=E((y_(i)−E(y_(i)))(y_(j)−E(y_(j)))), where E(y) is the expected valueof y, a matrix M which is the pseudoinverse matrix of A=W·Db·N, where Dbis the Jacobian matrix of the first measured variables y_(i)=b_(i)(x).5. The method in accordance with claim 4, wherein at least one of theselected parameters is a selected state variable which can be determinedvia the first measured variables, one or more of the first sensitivityvariables Φ_(yjxl) represents in each case the ratio of the change inaccuracy Δσ_(ll) ²/x₁=ΔE((x_(l)−E(x_(l)))²)/x_(l) of the selected statevariable x₁ to the change in accuracy Δσ_(ij)²/y_(j)=ΔE((y_(j)−E(y_(j)))²)/y_(j) of a first measured variable y_(j),the first sensitivity variables are determined from the followingformula:$\Phi_{y_{j}x_{l}} = {\frac{\sigma_{jj}^{2}}{\sigma_{ll}^{2}} \cdot r_{lj}^{2}}$where r_(lj) is the element in the l^(th) line and the j^(th) column ofthe matrix N·M·W.
 6. The method in accordance with claim 5, wherein oneof the selected parameters is a selected diagnostic variable which canbe determined via the first measured variables, a matrix Dd isdetermined, this being the Jacobian matrix of the diagnostic variablesd_(i)=d_(i)(x), one or more of the first sensitivity variables Φ_(yj dn)represents in each case the ratio of the change in accuracy Δσ_(nn)²/d_(n)=ΔE((d_(n)−E(d_(n)))²)/d_(n) of the selected diagnostic variabled_(n) to the change in accuracy Δσ_(ij)²/y_(j)=ΔE((y_(j)−E(y_(j)−E(y_(j)))²)/y_(j) of a first measured variabley_(j), the first sensitivity variables are determined by the followingformula:$\Phi_{y_{j}d_{n}} = {\frac{\sigma_{jj}^{2}}{\sigma_{nn}^{2}} \cdot s_{nj}^{2}}$where s_(nj) is the element in the n^(th) line and the j^(th) column ofDd·N·M·W.
 7. The method in accordance with claim 6, wherein at least oneof the selected parameters is a selected state variable which can bedetermined via the first measured variables, one or more of the secondsensitivity variables represents, in each case, the variance σ_(k→xl) ²of the selected state variable x_(l) when a second measured variable,the value of which is a state variable x_(k) with the variance σ_(k), isbeing added to the measurement park, the second sensitivity variablesare determined by the following formula:${\sigma_{k->{xl}}^{2} = {{m_{l}^{T} \cdot m_{l}} - \frac{\left( {m_{k}^{T} \cdot m_{l}} \right)^{2}}{\sigma_{k}^{2} + {m_{k}^{T} \cdot m_{k}}}}},$where m_(i) is the i^(th) column of the matrix M^(T)·N.
 8. The method inaccordance with claim 7, wherein at least one of the selected parametersis a selected diagnostic variable which can be determined via the firstmeasured variables, a matrix Dd, which is the Jacobian matrix of thediagnostic variables d_(i)=d_(i)(x), is determined, one or more of thesecond sensitivity variables represents, in each case, the varianceσ_(k→dn) ² of the selected diagnostic variable d_(n) when a secondmeasured variable, the value of which is a state variable x_(k) whichhas a variance σ_(k), is being added to the measurement park, the secondsensitivity variables are determined by the following formula:$\sigma_{k->{dn}}^{2} = {{q_{n}^{T} \cdot q_{n}} - \frac{\left( {m_{k}^{T} \cdot q_{n}} \right)^{2}}{\sigma_{k}^{2} + {m_{k}^{T} \cdot m_{k}}}}$where m_(i) is the i^(th) column of the matrix M^(T)·N^(T), and q_(n) isthe n^(th) column of the matrix and M^(T)·N^(T)·Dd^(T).
 9. The method inaccordance claim 8, wherein at least one of the selected parameters is aselected state variable which cannot be determined via the firstmeasured variables, a matrix P, which is the orthogonal projection ontothe null space of A, is determined, a second measured variable isdetermined, the value of which is a state variable x_(k), and which isto be added into the measurement park so that the selected statevariable can be uniquely determined, one of the second sensitivityvariables represents the variance σ_(k→xl) ² of the selected statevariable when the second measured variable x_(k) which has beendetermined, and which has the variance σ_(k), is being added to themeasurement park, the second sensitivity variable is determined by thefollowing formula:${\sigma_{k->{x\; 1}}^{2} = {{\sigma_{k}^{2} \cdot \frac{{p}^{2}}{{p_{k}}^{2}}} + {{m_{l} - {\frac{p}{p_{k}}m_{k}}}}^{2}}},$with p=Pn_(l), where n_(l) is the l^(th) column of the matrix N^(T), andm_(i) is the i^(th) column of the matrix M^(T)·N^(T) and p_(k) is thek^(th) column of the matrix P·N^(T).
 10. The method in accordance withclaim 9, wherein at least one of the selected parameters is a selecteddiagnostic variable which cannot be determined via the first measuredvariables, a matrix Dd, which is the Jacobian matrix of the diagnosticvariables d_(i)=d_(i)(x), is determined, a matrix P, which is theorthogonal projection onto the null space of A, is determined, a secondmeasured variable is determined, the value of which is a state variablex_(k), and which is to be added into the measurement park so that theselected state variable can be uniquely determined, one of the secondsensitivity variables represents the variance σ_(k→dn) ² of the selecteddiagnostic variable d_(n) when the second measured variable x_(k) whichhas been determined, and which has the variance σ_(k), is being addedinto the measurement park, the second sensitivity variable is determinedby the following formula:${\sigma_{k->{dn}}^{2} = {{\sigma_{k}^{2} \cdot \frac{{p}^{2}}{{p_{k}}^{2}}} + {{{M^{T} \cdot c_{n}} - {\frac{p}{p_{k}}m_{k}}}}^{2}}},$with p=Pc_(n), where c_(n) is the n^(th) column of the matrixN^(T)·Dd^(T), m_(k) is the k^(th) column of the matrix M^(T)·N^(T) andp_(k) is the k^(th) column of the matrix P·N^(T).
 11. The method inaccordance with claim 10, wherein the matrix P·N^(T) is searched for thecolumn such that p is a linear function of this column, where the indexk of this column specifies that the second measurement value x_(k) is tobe added into the measurement park so that the selected parameter can beuniquely determined.
 12. The method in accordance with claim 11, whereinthe standard deviation σ_(k) of the second measured variable is 1% ofthe value of the second measured variable.
 13. A device for analyzing atechnical system, comprising: a storage medium; a computer programstored on the storage medium, wherein the computer program comprisescomputer-readable code comprising; a software module for specifying thetechnical system by a system of equations and a plurality of statevariables being the solutions of the system of equations; a softwaremodule for analyzing a measurement park, incorporating a first measuredvariable and the first measured variable is measured in the technicalsystem with a prescribed accuracy and depends on the state variables; asoftware module for measuring a second measured variable, which dependson the state variables, in the technical system with a predeterminedaccuracy; a software module for determining sensitivity variables forthe first measured variable and/or the second sensitivity variable forthe second measured variable; determining the magnitude of the influencewhich a change in the accuracy of measurement of the first measuredvariable has at least one selected parameter to determine the firstsensitivity variable, and to determine the second sensitivity variable,a determination is made of the magnitude of the influence which themeasurement of the second measured variable has at least one selectedparameter; a software module for changing the measurement park toproduce an amended measurement park, said changing depending on thefirst and/or second sensitivity variable, in such a way that theaccuracy of the first measured variable is changed and/or the firstmeasured variable is taken out of the measurement park and/or the secondmeasured variable is added into the measurement park; and a softwaremodule for outputting a signal indicative of the amended measurementpark for performing at least one of the following to design thetechnical system; selecting which respective state variables and/ordiagnostic variables of the technical system to measure, and determiningrespective measurement accuracies for state variables and/or diagnosticvariables of the technical system.
 14. The device in accordance withclaim 13, wherein the accuracy of the first measured variable isincreased if the first sensitivity variable for this measured variablelies within a predefined value range and/or the first measured variableis taken out of the measurement park if the first sensitivity variablefor this measured variable lies within a predefined value range and/orthe second measured variable is added into the measurement park if thesecond sensitivity variable for this measured variable lies within apredefined value range.
 15. The device in accordance with claim 14,wherein the technical system is described by a system of equationsH(x)=(H₁(x), . . . , H_(m)(x))=0, where x=(x₁, . . . , x_(n)) is avector in which the components are the state variables x_(i).
 16. Thedevice in accordance with claim 15, wherein the following matrices arecalculated: a matrix N, which spans the null space of the Jacobianmatrix of H, a matrix W, such that W^(T)·W is the inverse of thecovariance matrix of the first measured variables y_(i)=b_(i)(x), wherethe entries in the covariance matrix are the covariances σ_(ij)²=E((y_(i)−E(y_(i)))(y_(j)−E(y_(j)))), where E(y) is the expected valueof y, a matrix M which is the pseudoinverse matrix of A=W·Db·N, where Dbis the Jacobian matrix of the first measured variables y_(i)=b_(i)(x).